Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Abstract

'Assessment of a seasonal and spatial variation of plant biophysical and spectral indices in the context of CO 2 , CH 4 , H 2 O gas exchange between wetland, forest and arable ecosystems and the atmosphere', 2010-2013.A b s t r a c t. The aim of this paper is to demonstrate that spectral vegetation indices are good indicators of parameters describing the intensity of CO 2 exchange between crops and the atmosphere. Measurements were conducted over 2011-2013 on plots of an experimental arable station on winter wheat, winter rye, spring barley, and potatoes. CO 2 fluxes were measured using the dynamic closed chamber system, while spectral vegetation indices were determined using SKYE multispectral sensors. Based on spectral data collected in 2011 and 2013, various models to estimate net ecosystem productivity and gross ecosystem productivity were developed. These models were then verified based on data collected in 2012. The R 2 for the best model based on spectral data ranged from 0.71 to 0.83 and from 0.78 to 0.92, for net ecosystem productivity and gross ecosystem productivity, respectively. Such high R 2 values indicate the utility of spectral vegetation indices in estimating CO 2 fluxes of crops. The effects of the soil background turned out to be an important factor decreasing the accuracy of the tested models.

Improving remote estimation of winter crops gross ecosystem production by inclusion of leaf area index in a spectral model

“…It has to be highlighted that sums of precipitation in May and June of 2011 - the two most critical for the plant growth months - were nearly two-times smaller than during the same period of the two following years. Considering the long term climatological records for this region, 2011 and 2013 are considered as warm (with the mean annual temperatures of 9.4 °C and 8.7 °C, respectively) and dry years (507 mm and 503 mm, respectively), while 2012 was considered as a warm (with the mean annual temperature of 9.0 °C) and wet (592 mm) year ( Uździcka et al, 2017 ). During the growing period of the main crop (March-August) the average PAR d was 659 (±241), 664 (±224) and 641 (±223) µmol m −2 s −1 in 2011, 2012 and 2013, respectively, with the maximum values of 1,011–1,068 µmol m −2 s −1 ( Fig.…”

“…Most of the remote sensing-based models to estimate GEP of croplands are rather crop-specific. They were developed for maize, soybean ( Gitelson et al, 2012 ), rice ( Inoue et al, 2008 ), wheat ( Wu et al, 2009 ), rye, barley, potato ( Uździcka et al, 2017 ) and in the majority of studies it has not been tested if these models can be directly applied (without reparametrization) for estimation of CO 2 uptake for other crops. Here we presented a more general and robust approach based on combining the datasets for the two winter and two spring crops together and we proved that the accuracy of crop-combined models is not different from those developed for winter crops on a separate basis (NRMSE of the best crop-combined models are in range of 13–16%), but it may be lowered compared to simple VI -based models developed for spring barley and potato (NRMSE is in range of about 11% for spring crops, whereas NRMSE of the best crop-combined model is 13.57%, Table 4 ).…”

“…Chamber measurements were taken every 3–5 weeks throughout the entire year (including winter) on cloudless days from sunrise to late afternoon ( Uździcka et al, 2017 ). However, when any clouds appeared (usually in the afternoon) a particular attention was paid to perform measurements at stable PAR conditions.…”

“…Both the seasonal dynamics and relationships between VIs and biophysical parameters have been analyzed in numerous studies. The relationships of spectral data have been investigated in relation to LAI ( Baret & Guyot, 1991 ; Law & Waring, 1994 ; Spanner et al, 1990 ; Zheng & Moskal, 2009 ), fAPAR ( Asrar et al., 1984 ; Wang et al, 2004 ; Sakowska, Juszczak & Gianelle, 2016 ), and the CO 2 fluxes exchanged between the ecosystem and the atmosphere – particularly GEP ( Rossini et al, 2012 ; Sakowska et al, 2014 ; Skinner, Wylie & Gilmanov, 2011 ; Uździcka et al, 2017 ), NEP ( Hassan, Bourque & Meng, 2006 ; Propastin & Kappas, 2009 ; Veroustraete, Patyn & Myneni, 1996 ) and NPP ( Gower, Kucharik & Norman, 1999 ; Hunt, 1994 ; Paruelo et al, 1997 ; Ruimy, Saugier & Dedieu, 1994 ). These kind of relationships were mostly analyzed for individual ecosystems (e.g., grasslands - Rossini et al, 2012 ; Sakowska et al, 2014 , peatlands - Chojnicki, 2013 , savanna - Sjöström et al, 2009 , forests - Xiao et al, 2004 ), or crop species on a separate basis (e.g., maize - Gitelson et al, 2003 ; Gitelson et al, 2014 ; Peng et al, 2011 , maize and soybean - Gitelson et al, 2012 , rice - Inoue et al, 2008 , wheat - Wu et al, 2009 ).…”

Peer Review #2 of "Improving remote estimation of winter crops gross ecosystem production by inclusion of leaf area index in a spectral model (v0.2)"

“…Both the seasonal dynamics and relationships between vegetation indices and biophysical parameters have been analyzed in numerous studies. The relationships of spectral data have been investigated in relation to LAI (Baret and Guyot, 1991, Law and Waring 1994, Spanner et al 1990, Zheng and Moskal 2009, fAPAR (Asrar et al 1989, Wang et al 2004, and the CO 2 fluxes exchanged between the ecosystem and the atmosphere -particularly GEP (Rossini et al 2012, Sakowska et al 2014, Skinner et al 2011, Uździcka et al 2017, NEP (Hassan et al 2006, Propastin and Kappas 2009, Veroustraete et al 1996 and NPP (Gower et al 1999, Hunt 1994, Paruelo et al 1997, Ruimy et al 1994). These kind of relationships were analyzed for individual ecosystems (e.g.…”